LncRNA PCAT7 promotes the malignant progression of breast cancer by regulating ErbB/PI3K/Akt pathway

Effect of ZIC2 on immune infiltration and ceRNA axis regulation in lung adenocarcinoma via bioinformatics and experimental studies

OBJECTIVE

This study aimed to conclude the effect and mechanism of ZIC2 on immune infiltration in lung adenocarcinoma (LUAD).

METHODS

Expression of ZIC2 in several kinds of normal tissues of TCGA data was analyzed and its correlation with the baseline characteristic of LUAD patients were analyzed. The immune infiltration analysis of LUAD patients was performed by CIBERSORT algorithm. The correlation analysis between ZIC2 and immune cell composition was performed. Additionally, the potential upstream regulatory mechanisms of ZIC2 were predicted to identify the possible miRNAs and lncRNAs that regulated ZIC2 in LUAD. In vitro and in vivo experiments were also conducted to confirm the potential effect of ZIC2 on cell proliferation and invasion ability of LUAD cells.

RESULTS

ZIC2 expression was decreased in various normal tissues, but increased in multiple tumors, including LUAD, and correlated with the prognosis of LUAD patients. Enrichment by GO and KEGG suggested the possible association of ZIC2 with cell cycle and p53 signal pathway. ZIC2 expression was significantly correlated with T cells CD4 memory resting, Macrophages M1, and plasma cells, indicating that dysregulated ZIC2 expression in LUAD may directly influence immune infiltration. ZIC2 might be regulated by several different lncRNA-mediated ceRNA mechanisms. In vitro experiments validated the promotive effect of ZIC2 on cell viability and invasion ability of LUAD cells. In vivo experiments validated ZIC2 can accelerate tumor growth in nude mouse.

CONCLUSION

ZIC2 regulated by different lncRNA-mediated ceRNA mechanisms may play a critical regulatory role in LUAD through mediating the composition of immune cells in tumor microenvironment.

A novel prognostic model of breast cancer based on cuproptosis-related lncRNAs

OBJECTIVE

Breast cancer (BC) is a deadly form of malignancy responsible for the death of a large number of women every year. Cuproptosis is a newly discovered form of cell death that may have implications for the prognosis of BC. Long non-coding RNAs (lncRNAs) have been shown to be involved in the progression and development of BC. Here within, a novel model capable of predicting the prognosis of patients with BC was established based on cuproptosis-related lncRNAs.

METHODS

Data of breast cancer patients was downloaded, including clinical information from The Cancer Genome Atlas (TCGA) database and lncRNAs related to cuproptosis were isolated. In total, nine lncRNAs related to copper death were obtained by Cox regression model based on Least Absolute Shrinkage and Selector Operation (LASSO) algorithm for model construction. The model was verified by overall survival (OS), progression-free survival (PFS) and receiver operating characteristic (ROC) curve. The differences in immune function, tumor mutation burden (TMB) and tumor immune dysfunction and exclusion (TIDE) between patients with different risk scores were analyzed.

RESULTS

Based on cuproptosis-related lncRNAs, a prognostic model for predicting BC was constructed. Each patient was assigned a risk score based on our model formula. We found that patients with higher risk scores had significantly lower OS and PFS, increased TMB, and higher sensitivity to immunotherapy.

CONCLUSIONS

The model established in this study based on cuproptosis-related lncRNAs may be capable of improving the OS of patients with BC.

Identification of CD4+ Conventional T Cells-Related lncRNA Signature to Improve the Prediction of Prognosis and Immunotherapy Response in Breast Cancer

Background

Breast cancer (BC) is one of the most common malignancies in women, and long non-coding RNAs (lncRNAs) are key regulators of its development. T cells can recognize and kill cancer cells, and CD4⁺ T conventional (Tconv) cells are the main orchestrators of cancer immune function. However, research on CD4⁺ Tconv-related lncRNAs (CD4TLAs) prognostic signature in patients with BC is still lacking.

Method

A TCGA database and a GEO database were used to collect the BC patients. Through LASSO Cox regression analysis CD4TLAs-related prognostic models were further constructed, and risk scores (RS) were generated and developed a nomogram based on CD4TLAs. The accuracy of this model was validated in randomized cohorts and different clinical subgroups. Gene set enrichment analysis (GSEA) was used to explore potential signature-based functions. The role of RS has been further explored in the tumor microenvironment (TME), immunotherapy, and chemotherapy.

Result

A prognostic model based on 16 CD4TLAs was identified. High-RS was significantly associated with a poorer prognosis. RS was shown to be an independent prognostic indicator in BC patients. The low-RS group had a significant expression of immune infiltrating cells and significantly enriched immune-related functional pathways. In addition, the results of immunotherapy prediction indicated that patients with low-RS were more sensitive to immunotherapy.

Conclusions

Our signature has potential predictive value for BC prognosis and immunotherapy response. The findings of this work have greatly increased our understanding of CD4TLA in BC.

High RRM2 expression has poor prognosis in specific types of breast cancer

BACKGROUND

RRM2 plays an important role in different malignant tumors, but there are few studies in breast cancer. Public databases were used to analyze the expression of RRM2 in breast cancer and its prognostic value.

MATERIALS AND METHODS

A total of 2,509 breast cancer samples were downloaded from the METABRIC database. The relationship between RRM2 expression and clinical pathology was evaluated. Using the BCIP database and real-time-PCR, and western blotting, RRM2 mRNA and protein expression of RRM2 in breast cancer tissues and cell lines were evaluated. Univariate and multivariate analysis defined independent prognostic factors that affected the overall survival of patients with breast cancer. The Kaplan-Meier method was used to study the relationship between the high expression of RRM2 and overall survival and distant metastasis-free survival (DMFS) of breast cancer patients. Finally, We performed Gene Set Enrichment Analysis (GSEA) and obtained the relevant pathways associated with high expression of RRM2 potentially influencing breast cancer progression.

RESULTS

RRM2 expression was significantly correlated with age, tumor size, grade, menopausal status, molecular typing, ER, PR, and Her-2 of patients with breast cancer(P<0.05). Univariate and multivariate regression analysis showed that RRM2, the number of positive lymph nodes, ER, Her-2, tumor size, and tumor stage can be used as independent prognostic factors for overall survival of patients with breast cancer. Kaplan-Meier analysis showed that in patients with Luminal A and Normal like breast cancers and Stage1 and stage2 breast cancers, patients with high expression of RRM2 had worse overall survival and DMFS. The analysis of the GSEA pathway showed that RRM2 is mainly enriched in the ERBB signaling pathway and other pathways.

CONCLUSION

The high expression of RRM2 has a worse prognosis in patients with breast cancer with specific features. It can be used as a biomarker for the prognosis of breast cancer.

5-Methylcytosine-Related Long Noncoding RNAs Are Potential Biomarkers to Predict Overall Survival and Regulate Tumor-Immune Environment in Patients with Bladder Cancer

The role of 5-methylcytosine-related long noncoding RNAs (m5C-lncRNAs) in bladder cancer (BLCA) remains unclear. Here, we aim to study the prognostic value, gene expression characteristics, and correlation between the m5C-lncRNA risk model and the tumor microenvironment, immune infiltration, and tumor mutations in BLCA. After collecting BLCA patient RNA sequence transcriptome data, clinical information and mutation data from the Cancer Genome Atlas (TCGA) database, 17 m5C-related lncRNAs independently correlated with OS were obtained by Lasso and multivariate Cox regression analysis, and a risk model was constructed. Univariate Cox, multivariate Cox regression analysis, and the C-index curve proved that the risk model was a significant independent prognostic indicator for patients with BLCA. ESTIMATE and CIBERSORT indicated that the higher the number of immune cells and stromal cells in TME, the higher the prognostic risk. We found that in the low-risk group, the expression levels of immune cells that predicted a good prognosis were higher, including plasma cells, regulatory T cells, and CD 8 T cells. There is a negative correlation between TMB and risk score. The TMB of the low-risk group is significantly higher than that of the high-risk group. In conclusion, the m5C-related risk model is crucial to predict the prognosis of patients with BLCA.

Lysophosphatidic acid receptor 6 regulated by miR-27a-3p attenuates tumor proliferation in breast cancer

Purpose

Lysophosphatidic acid (LPA) is a bioactive molecule which participates in many physical and pathological processes. Although LPA receptor 6 (LPAR6), the last identified LPA receptor, has been reported to have diverse effects in multiple cancers, including breast cancer, its effects and functioning mechanisms are not fully known.

Methods

Multiple public databases were used to investigate the mRNA expression of LPAR6, its prognostic value, and potential mechanisms in breast cancer. Western blotting was performed to validate the differential expression of LPAR6 in breast cancer tissues and their adjacent tissues. Furthermore, in vitro experiments were used to explore the effects of LPAR6 on breast cancer. Additionally, TargetScan and miRWalk were used to identify potential upstream regulating miRNAs and validated the relationship between miR-27a-3p and LPAR6 via real-time polymerase chain reaction and an in vitro rescue assay.

Results

LPAR6 was significantly downregulated in breast cancer at transcriptional and translational levels. Decreased LPAR6 expression in breast cancer is significantly correlated with poor overall survival, disease-free survival, and distal metastasis-free survival, particularly for hormone receptor-positive patients, regardless of lymph node metastatic status. In vitro gain and loss-of-function assays indicated that LPAR6 attenuated breast cancer cell proliferation. The analyses of TCGA and METABRIC datasets revealed that LPAR6 may regulate the cell cycle signal pathway. Furthermore, the expression of LPAR6 could be positively regulated by miR-27a-3p. The knockdown of miR-27a-3p increased cell proliferation, and ectopic expression of LPAR6 could partly rescue this phenotype.

Conclusion

LPAR6 acts as a tumor suppressor in breast cancer and is positively regulated by miR-27a-3p.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12094-021-02704-8.